Journal
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
Volume 28, Issue 9, Pages 1908-1920Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNSRE.2020.3003342
Keywords
Feature extraction; Decoding; Learning systems; Computer architecture; Sparse matrices; Data mining; Biological neural networks; Broad learning system; local field potentials; action potentials; multi-view learning; primate oculomotor decision
Categories
Funding
- National Institutes of Health [2R01NS086104, 1R01DA040990]
- Hubei Technology Innovation Platform [2019AEA171]
- National Natural Science Foundation of China [61873321, U1913207]
- 111 Project on Computational Intelligence and Intelligent Control [B18024]
- International Science and Technology Cooperation Program of China [2017YFE0128300]
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Multi-view learning improves the learning performance by utilizing multi-view data: data collected from multiple sources, or feature sets extracted from the same data source. This approach is suitable for primate brain state decoding using cortical neural signals. This is because the complementary components of simultaneously recorded neural signals, local field potentials (LFPs) and action potentials (spikes), can be treated as two views. In this paper, we extended broad learning system (BLS), a recently proposed wide neural network architecture, from single-view learning to multi-view learning, and validated its performance in decoding monkeys' oculomotor decision from medial frontal LFPs and spikes. We demonstrated that medial frontal LFPs and spikes in non-human primate do contain complementary information about the oculomotor decision, and that the proposed multi-view BLS is a more effective approach for decoding the oculomotor decision than several classical and state-of-the-art single-view and multi-view learning approaches.
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